Results:
Out of the original 23 attributes,
10 are useless, if not harmful, for the correct classification of the type of
unbalance. The highest classification accuracy was achieved when the
following 13 inputs were used: 1) RtSp; 2) A04D1; 3) A04D2; 4)
A18D1; 5) A18D2;
6) R02D1; 7) R16D1; 8) R16D2; 9) R16TZ (obtained as
R02TZ - RdifTZ); 10) node1_#; 11) node2_#; 12) unbal1; and 13)
unbal2. The confusion matrix for four runs using only these 13
inputs are shown below.

Among the 23 attributes originally measured for the classification (or the
diagnosis) of unbalance types, some are redundant, if not conflicting. To
achieve the highest classification accuracy, a subset of only 13 attributes is
necessary. A subset with only 8 attributes (or almost only one third of
the original set) can be used to achieve an overall classification accuracy
around 97%.

From the confusion matrices, it can be seen that: Among the five
classes (or unbalance types), Classes b and m are the easiest to
identify. Some samples from Class d are sometimes misidentified as
Class m and a few time as Class q. Samples from Classes s and q are
rarely classified into any of the other three classes, but it quite
difficult to identify some samples between these two classes. Perhaps
from the mechanics point of view, these two classes are the similar to each
other?